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jeremykun.wordpress.com | ||
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www.ayoub-benaissa.com
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| | | | | This is the first of a series of blog posts about the use of homomorphic encryption for deep learning. Here I introduce the basics and terminology as well as link to external resources that might help with a deeper understanding of the topic. | |
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www.jeremykun.com
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| | | | | The Learning With Errors problem is the basis of a few cryptosystems, and a foundation for many fully homomorphic encryption (FHE) schemes. In this article I'll describe a technique used in some of these schemes called modulus switching. In brief, an LWE sample is a vector of values in $\mathbb{Z}/q\mathbb{Z}$ for some $q$, and in LWE cryptosystems an LWE sample can be modified so that it hides a secret message $m$. | |
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windowsontheory.org
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| | | | | Guest post by Boaz Barak and Zvika Brakerski (part 2) In the previous post, we demonstrated the versatility of fully homomorphic encryption and its applicability for multiple applications. In this post we will demonstrate (not too painfully, we hope) how fully homomorphic encryption is constructed. Our goal is to present the simplest solution that (we... | |
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www.daniellowengrub.com
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| | | [AI summary] The text discusses the implementation of homomorphic operations in the context of RLWE (Ring Learning With Errors) and GSW (Gentry-Sahai-Waters) encryption schemes. Key concepts include the use of encryptions of zero to facilitate homomorphic multiplication, the structure of GSW ciphertexts as matrices of RLWE ciphertexts, and the role of scaling factors to manage error growth during multiplication. The main goal is to enable secure computation of polynomial products without revealing the underlying plaintexts. | ||
